But chasing the higher hanging fruits might allow for breakthroughs that you would not see if you only went for the low hanging fruits.
The range anxiety is less and less problematic with EV. The Tesla Roadster 2 already is said to have a range of more than 1.000 km. Add current research in the fields of solid state batteries and super capacitators and you have the possibility to reach those numbers even with less expensive versions of EV. German automakers already calculate that by 2026 electric engines will be cheaper and more capable than their ICE counterpart.
If you go for that easy middle ground like hybrid cars that you suggest, you limit yourself to the local maximum of that solution. Hybrid cars have the same maintenance cost as non hybrid cars and additionally the complexity of balancing both engines. The only saving in maintenance cost is by going full electric. In the same way you might only achieve certain breakthroughs by actually going for full autonomy even if it wont work perfectly for the next decades for all edge cases.
What made you leave? Did you miss european culture ( or sports on TV or anything like that)? How did you deal with the time difference between Europe and NZ? When I was in NZ on vacation I missed being able to watch the stuff that I am used to on TV in the evening, due to the twelve hour time difference to Europe. I dont think I could ever switch from soccer to cricket.
Simple example? Before finishing the first paragraph, it says
"The slope and y-intercept of the line are determined using gradient descent."
What on earth does that mean? Maybe they should teach mathematics in english at universities outside of english speaking countries. German mathematics does not help here.
I wish there was a 4GL like SQL for machine learning using dynamic programming for algorithm selection and model synthesis like a dbms query planner.
PREDICT s as revenue
LEARN FROM company.sales as s
GROUP BY MONTH
ORDER BY company.region
The first eight minutes were promising. Then a mathematical definiton of how neural networks work follows and I am already lost again when trying to learn about deep learning. It is like trying to learn Postgresql's new JSON features by starting to look at relational calculus first.
Well it is only useful when you really rely on asynchronous programming. Nobody states that every piece of code is supposed to be written like this. You should only use async/await when a thorough performance analysis shows that it is your bottleneck.
Think of handling a web request, where you have to do parallel I/O requests to subsystems like a database, a webservice, redis, and so on. I think async/await gives us a nice standard way of describing "hit me back once X is done".